In [1]:
# If you are highly active on Facebook and Instagram, you must have seen ads based on elections by any of the political parties,
# especially BJP and INC. All the parties in India usually spend a lot of money on election campaigns. I recently collected data
# from Meta ads about how much money was spent on Instagram and Facebook ads by each political party during the Indian elections
# 2024 in each state. In this article, we perform elections ad spending analysis using Python 
# and how it impactedthe voting patterns.
In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [5]:
results = pd.read_csv(r"D:\Testdirectory\Elections AD Spend Analysis\results.csv")
advertisers = pd.read_csv(r"D:\Testdirectory\Elections AD Spend Analysis\advertisers.csv")
locations = pd.read_csv(r"D:\Testdirectory\Elections AD Spend Analysis\locations.csv")
In [7]:
results.head(5)
Out[7]:
_id Sl No State PC_Name Total Electors Polled (%) Total Votes Phase
0 1 1.0 Andaman & Nicobar Islands Andaman & Nicobar Islands 315148 64.10 202018 1.0
1 2 2.0 Arunachal Pradesh Arunachal East 375310 83.31 312658 1.0
2 3 3.0 Arunachal Pradesh Arunachal West 517384 73.60 380783 1.0
3 4 4.0 Assam Dibrugarh 1659588 76.75 1273744 1.0
4 5 5.0 Assam Jorhat 1727121 79.89 1379749 1.0
In [8]:
advertisers.head(5)
Out[8]:
Page ID Page name Disclaimer Amount spent (INR) Number of ads in Library
0 121439954563203 Bharatiya Janata Party (BJP) Bharatiya Janata Party (BJP) 193854342 43455
1 351616078284404 Indian National Congress Indian National Congress 108787100 846
2 132715103269897 Ama Chinha Sankha Chinha Ama Chinha Sankha Chinha 73361399 1799
3 192856493908290 Ama Chinha Sankha Chinha Ama Chinha Sankha Chinha 32294327 680
4 109470364774303 Ellorum Nammudan Populus Empowerment Network Private Limited 22399499 879
In [9]:
locations.head(5)
Out[9]:
Location name Amount spent (INR)
0 Andaman and Nicobar Islands 377858
1 Andhra Pradesh 100819732
2 Arunachal Pradesh 1385654
3 Assam 17478091
4 Bihar 53619242
In [99]:
# The advertisers data contains:

# Page ID: A unique identifier for the advertiser’s page.
# Partyme: The name of the advertiser’s party
# Disclaimer: Information about the advertiser, typically who paid for the ads.
# Amount spent (INR): The total amount of money spent on ads in Indian Rupees.
# Number of ads in Library: The number of ads associated with the advertiser.
# The locations data contains:
    
# The locations data contains:

# Location name: The name of the location.
# Amount spent (INR): The total amount of money spent on ads in that location in Indian Rupees.
# The results data contains:
    
# The results data contains:

# _id: A unique identifier for the entry.
# Sl No: Serial number.
# State: The name of the state.
# PC_Name: The name of the parliamentary constituency.
# Total Electors: The total number of registered voters.
# Polled (%): The percentage of votes polled.
# Total Votes: The total number of votes cast.
# Phase: The phase of the election.

Gathering informations regarding the data¶

In [11]:
results.isnull().sum()
Out[11]:
_id                0
Sl No              7
State              7
PC_Name            0
Total Electors     0
Polled (%)         0
Total Votes        0
Phase             58
dtype: int64
In [12]:
advertisers.isnull().sum()
Out[12]:
Page ID                     0
Page name                   0
Disclaimer                  0
Amount spent (INR)          0
Number of ads in Library    0
dtype: int64
In [13]:
locations.isnull().sum()
Out[13]:
Location name         0
Amount spent (INR)    0
dtype: int64
In [26]:
# phase, sno and state have 58 null values
In [25]:
sns.heatmap(results.isnull())
Out[25]:
<Axes: >
In [18]:
results.shape, advertisers.shape, locations.shape
Out[18]:
((550, 8), (20832, 5), (36, 2))
In [20]:
results[results.duplicated()]
Out[20]:
_id Sl No State PC_Name Total Electors Polled (%) Total Votes Phase
In [21]:
advertisers[advertisers.duplicated()]
Out[21]:
Page ID Page name Disclaimer Amount spent (INR) Number of ads in Library
In [22]:
locations[locations.duplicated()]
Out[22]:
Location name Amount spent (INR)
In [23]:
# no duplicate values

Merging results and locations¶

In [27]:
results['State'] = results['State'].str.strip().str.lower()
In [30]:
locations['Location name'] = locations['Location name'].str.strip().str.lower()
In [37]:
#  The 'State'  in results and 'Location name'  in Locations is cleaned by stripping whitespace and converting to lowercase.
In [32]:
merged_data = results.merge(locations, left_on = 'State', right_on = 'Location name', how = 'left')
In [33]:
merged_data.sample(5)
Out[33]:
_id Sl No State PC_Name Total Electors Polled (%) Total Votes Phase Location name Amount spent (INR)
129 130 27.0 karnataka Tumkur 1661309 78.05 1296720 2.0 karnataka 41659397.0
516 517 25.0 punjab Bathinda 1651188 69.36 1145241 NaN NaN NaN
296 297 11.0 andhra pradesh Kakinada 1634122 80.30 1312255 4.0 andhra pradesh 100819732.0
240 241 49.0 karnataka Bidar 1892962 65.47 1239358 3.0 karnataka 41659397.0
519 520 28.0 punjab Firozpur 1670008 67.02 1119167 NaN NaN NaN
In [ ]:
# A new DataFrame merged_data is created that contains all rows from results with corresponding rows from locations
# where the 'State' column in results matches the 'Location name' column in locations
In [34]:
merged_data.isnull().sum()
Out[34]:
_id                    0
Sl No                  7
State                  7
PC_Name                0
Total Electors         0
Polled (%)             0
Total Votes            0
Phase                 58
Location name         31
Amount spent (INR)    31
dtype: int64
In [35]:
merged_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 550 entries, 0 to 549
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   _id                 550 non-null    int64  
 1   Sl No               543 non-null    float64
 2   State               543 non-null    object 
 3   PC_Name             550 non-null    object 
 4   Total Electors      550 non-null    int64  
 5   Polled (%)          550 non-null    float64
 6   Total Votes         550 non-null    int64  
 7   Phase               492 non-null    float64
 8   Location name       519 non-null    object 
 9   Amount spent (INR)  519 non-null    float64
dtypes: float64(4), int64(3), object(3)
memory usage: 43.1+ KB
In [36]:
sns.heatmap(merged_data.isnull())
Out[36]:
<Axes: >

Analysis of AD Spent by State¶

In [39]:
# importing required libraries

import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.templates.default = "plotly_white"
In [40]:
# data preparation

state_ad_spent = merged_data.groupby('State')['Amount spent (INR)'].sum().reset_index()
In [42]:
state_ad_spent.sample(5)
Out[42]:
State Amount spent (INR)
28 rajasthan 1.031708e+09
17 lakshadweep 1.984700e+04
34 uttarakhand 3.570448e+07
19 maharashtra 4.892020e+09
35 west bengal 3.244290e+09
In [46]:
# plot creation

fig = px.bar(state_ad_spent, x = 'State', y = 'Amount spent (INR)',
             labels = {'State' : 'State', 'Amount spent (INR)' : 'Amount spend (INR)'},
            title = 'Total AD Spend by State')
In [49]:
# customizing the plot

fig.update_layout(xaxis = {'categoryorder' : 'total descending'}, xaxis_tickangle = -90, width = 800, height = 600 )
In [ ]:
# The bar graph shows the total ad spend (in INR) by state. Uttar Pradesh leads significantly with the highest ad spend,
# followed by Maharashtra and Odisha. States like West Bengal, Tamil Nadu, Andhra Pradesh, and Bihar also show 
# substantial ad expenditures. In contrast, states such as Lakshadweep, Dadra & Nagar Haveli, Daman & Diu,
# Andaman & Nicobar Islands, and Arunachal Pradesh have the lowest ad spend.It indicates that larger and more populous 
# states tend to spend more on ads, likely reflecting their greater political significance and larger voter base.
In [51]:
state_ad_spent['Amount spent (INR)'].describe()
Out[51]:
count    3.600000e+01
mean     9.360097e+08
std      1.645426e+09
min      0.000000e+00
25%      1.710248e+06
50%      4.187547e+07
75%      1.063730e+09
max      7.173450e+09
Name: Amount spent (INR), dtype: float64
In [54]:
total_ad_spent = state_ad_spent['Amount spent (INR)'].sum()
print(f"Total Ad Spent: {total_ad_spent} INR")

Total Ad Spent: 33696348101.0 INR

Averagr voter turnout by state¶

In [56]:
# data preparation

state_voter_turnout = merged_data.groupby('State')['Polled (%)'].mean().reset_index()
In [57]:
state_voter_turnout.sample(5)
Out[57]:
State Polled (%)
6 chhattisgarh 73.105455
1 andhra pradesh 80.770800
7 dadra & nagar haveli and\ndaman & diu 70.645000
35 west bengal 79.188095
28 rajasthan 61.426400
In [60]:
# plot creation

fig = px.bar(state_voter_turnout,  x='State', y='Polled (%)',
             labels={'State': 'State', 'Polled (%)': 'Voter Turnout (%)'},
             title='Average Voter Turnout by State')
In [61]:
# customizing the data

fig.update_layout(xaxis={'categoryorder': 'total descending'},
                  xaxis_tickangle=-90,
                  width=800,
                  height=600)
In [62]:
# Lakshadweep has the highest average voter turnout at nearly 80%, followed closely by Tripura and Assam. 
# States like Andhra Pradesh, Sikkim, and West Bengal also show high voter engagement, with turnouts above 70%.
# On the other end of the spectrum, states such as Bihar, Uttar Pradesh, and Uttarakhand have the lowest average voter turnout,
# around 50-55%. It indicates significant regional variations in voter participation, with some smaller states
# and union territories exhibiting higher engagement compared to larger states with higher ad spend.

Top 5 parties by ad spent¶

In [63]:
# data preparation and cleaning

advertisers['Amount spent (INR)'] = pd.to_numeric(advertisers['Amount spent (INR)'], errors = 'coerce')
In [65]:
advertisers.dropna(subset = ['Amount spent (INR)'], inplace = True)
In [66]:
advertisers.rename(columns={'Page name': 'Party name'}, inplace=True)
In [72]:
advertisers.head(5)
Out[72]:
Page ID Party name Disclaimer Amount spent (INR) Number of ads in Library
0 121439954563203 Bharatiya Janata Party (BJP) Bharatiya Janata Party (BJP) 193854342.0 43455
1 351616078284404 Indian National Congress Indian National Congress 108787100.0 846
2 132715103269897 Ama Chinha Sankha Chinha Ama Chinha Sankha Chinha 73361399.0 1799
3 192856493908290 Ama Chinha Sankha Chinha Ama Chinha Sankha Chinha 32294327.0 680
4 109470364774303 Ellorum Nammudan Populus Empowerment Network Private Limited 22399499.0 879
In [74]:
# data aggregation and sorting

party_ad_spent = advertisers.groupby('Party name')['Amount spent (INR)'].sum().sort_values(ascending = False)
In [75]:
top_5_parties = party_ad_spent.head(5).reset_index()
In [76]:
top_5_parties
Out[76]:
Party name Amount spent (INR)
0 Bharatiya Janata Party (BJP) 193854342.0
1 Ama Chinha Sankha Chinha 112412941.0
2 Indian National Congress 108787100.0
3 Ellorum Nammudan 23806041.0
4 BJP Odisha 19573782.0
In [81]:
# visualization with plotly

colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0']

fig = px.pie(top_5_parties, values='Amount spent (INR)', names='Party name',
             title='Top 5 Parties by Ad Spend', color_discrete_sequence=colors,
             labels={'Party name': 'Political Party', 'Amount spent (INR)': 'Ad Spend (INR)'})

fig.update_traces(textinfo='percent')
In [82]:
fig.update_layout(
    showlegend=True,
    legend=dict(
        orientation="v",
        yanchor="top",
        y=1,
        xanchor="left",
        x=-0.3
    ),
    title=dict(
        y=0.95,
        x=0.5,
        xanchor='center',
        yanchor='top'
    ),
    margin=dict(l=200, r=50, t=100, b=50) 
)
In [83]:
# The Bharatiya Janata Party (BJP) has the highest ad spend, accounting for 42.3% of the total. This is followed by the 
# Ama Chinha Sankha Chinha party at 24.5% and the Indian National Congress at 23.7%. Ellorum Nammudan and BJP Odisha have
# significantly lower ad spends, at 5.19% and 4.27%, respectively. It indicates that BJP dominates in terms of ad spending
# on Facebook and Instagram ads, with nearly half of the total expenditure, suggesting a significant investment
# in advertising ompared to other parties.

Correlation between ad spend and voter turnout:¶

In [84]:
correlation = merged_data[['Amount spent (INR)', 'Polled (%)']].corr()
print(correlation)

                    Amount spent (INR)  Polled (%)
Amount spent (INR)            1.000000   -0.010688
Polled (%)                   -0.010688    1.000000
In [87]:
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation Matrix')
plt.show()
In [85]:
# The correlation matrix shows that the relationship between the amount spent (INR) and the percentage of votes polled (%) is
# very weak and slightly negative, with a correlation coefficient of -0.010688. This indicates that there is virtually no 
# linear relationship between ad spend and voter turnout. In other words, increasing the amount spent on advertising does not 
# significantly affect the percentage of voter turnout.
In [86]:
# The correlation coefficient ranges from -1 to 1.
# A coefficient close to 1 indicates a strong positive correlation (as one variable increases, the other tends to increase).
# A coefficient close to -1 indicates a strong negative correlation (as one variable increases, the other tends to decrease).
# A coefficient close to 0 indicates no linear correlation between the variables.

Relationship between ad spend and voter turnout by parliamentary constituency:¶

In [89]:
# visualization

fig = px.scatter(merged_data, x='Amount spent (INR)', y='Polled (%)',
                 color='State',
                 labels={'Amount spent (INR)': 'Ad Spend (INR)', 'Polled (%)': 'Voter Turnout (%)'},
                 title='Ad Spend and Voter Turnout by Parliamentary Constituency')

fig.update_layout(width=800, height=600)

fig.show()

D:\ANACONDA\lib\site-packages\plotly\express\_core.py:1979: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
In [92]:
# It shows that higher ad spending does not necessarily correlate with higher voter turnout. Voter turnout seems to cluster 
# between 60% and 80% across most constituencies, regardless of the ad spend amount, which ranges from 0 to 150 million INR.
# This suggests that other factors besides ad spend may play a significant role in influencing voter turnout.

Distribution of ad spending:¶

In [94]:
fig = px.histogram(merged_data, x = ['Amount spent (INR)'], nbins = 30, marginal = 'box',
                   labels = {'Amount spent (INR)' : 'Amount spend (INR)'}, 
                   title = 'Distribution of AD Spending')
In [96]:
fig.update_traces(marker = dict(line = dict(color='black', width=1)))
In [97]:
fig.update_layout(bargap=0.1, width=800, height=600)

fig.show()
In [98]:
# The histogram indicates that most constituencies have ad spends clustered around the 50M and 100M INR marks, with fewer
# constituencies spending less than 10M INR or more than 150M INR. The box plot highlights that the median ad spend is around
# 70M INR, with the interquartile range (IQR) spanning from approximately 30M to 110M INR. There are a few outliers, 
# particularly a constituency with an exceptionally high ad spend above 150M INR. This distribution suggests that while the 
# majority of ad spends are concentrated within a certain range, there are notable exceptions with significantly higher 
# expenditures.

Analysis of ad spending and voter turnout by election phase:¶

In [100]:
import plotly.graph_objects as go
In [101]:
# data preparation

phase_analysis = merged_data.groupby('Phase').agg({'Amount spent (INR)': 'sum', 'Polled (%)': 'mean'}).reset_index()
In [102]:
fig = go.Figure()
In [113]:
# Add Bar trace for Ad Spend (INR)
fig.add_trace(go.Bar(
    x=phase_analysis['Phase'],
    y=phase_analysis['Amount spent (INR)'],  
    name='Ad Spend (INR)',
    marker_color='indianred',
    yaxis='y1'
))

# Add Scatter trace for Voter Turnout (%)
fig.add_trace(go.Scatter(
    x=phase_analysis['Phase'],
    y=phase_analysis['Polled (%)'],     
    name='Voter Turnout (%)',
    marker_color='lightsalmon',
    yaxis='y2'
))

# Update layout of the figure
fig.update_layout(
    title='Ad Spend and Voter Turnout by Election Phase',
    xaxis=dict(title='Election Phase'),
    yaxis=dict(                                            
        title='Ad Spend (INR)',
        titlefont=dict(color='indianred'),                 
        tickfont=dict(color='indianred')                   
    ),
    yaxis2=dict(                                           
        title='Voter Turnout (%)',
        titlefont=dict(color='lightsalmon'),               
        tickfont=dict(color='lightsalmon'),                
        overlaying='y',
        side='right'                                       
    ),
    legend=dict(x=0.1, y=1.1, orientation='h'),
    width=800,
    height=600
)

fig.show()
In [114]:
# There is no consistent trend between ad spend and voter turnout. Election phases 1 and 4 have the highest ad spends, with phase 
# 4 peaking in voter turnout at around 70%. However, phase 1, despite high ad spend, has a lower voter turnout of about 67%. 
# Phases with moderate ad spend (e.g., 2 and 6) have lower voter turnout, while phase 5 has a notably low turnout despite 
# moderate spending.

Conclusion¶

In [116]:
# Overall, the analyses indicate that higher ad spend does not guarantee higher voter turnout and voter engagement is influenced
# by various other factors. Larger and more significant states tend to spend more on ads, but this does not necessarily
# translate to higher voter participation. Political parties, particularly the BJP, invest heavily in advertising,
# yet the effectiveness of this spending in increasing voter turnout is questionable
In [ ]: